1. Field of the Invention
The disclosure relates to methods for quantitative assessment of a patient-specific immune system state in tumor tissue and other relevant tissue compartments for evaluating a potential or ongoing treatment; and more particularly to such methods including analyzing inflammatory cell quantity, expression level of biomarkers or quantity of mediators of inflammation, and the distribution pattern of inflammatory cells in distinct tissue compartments within tissue samples containing tumor tissue.
2. Description of the Related Art
The role of the immune system in response to cancer treatment is becoming more evident. Research studies have shown that the localization of specific inflammatory cell types in or near tumor tissue can be a prognostic factor for an array of cancer types. Furthermore, some cancer therapies induce an inflammatory response near the tumor, and it is suggested that this response is an indicator of treatment outcome. Thus, a cursory characterization of individual populations of inflammatory cells (e.g. CD8+ and CD45RO+ cells) and modulators of the inflammatory response (e.g. cytokines and chemokines) has identified patient-specific immune system landscapes, in the context of tumor tissue, which could influence patient care.
While current evidence indicates that the immune system state in tumor tissues, as reflected by individual inflammation modulators, impacts prognosis, functional studies and current biologic models of the immune system-tumor interaction suggest that the immune system state could be predictive of patient responses to specific therapies. This paradigm suggests that an in-depth characterization of inflammatory cell subpopulations and mediators of inflammation could be used to select patients who are more likely to respond to specific therapies based on their immune system landscape. Therefore, an accurate method for profiling the quantity and distribution of inflammatory cell types and mediators of inflammation within the tumor and surrounding tissue would become necessary to stratify patients in this manner.
Manual evaluation of histologic tissue sections by a pathologist is commonly implemented to assess inflammatory cells and modulators of inflammation. The evaluation of a tissue section by a pathologist can involve determining the quantity of inflammatory cells and scoring expression levels of molecules that modulate inflammation in a tissue compartment, for example, tumor tissue or other relevant tissue types, such as stromal tissue. However, the intricate spatial relationships and the often complex distribution of inflammatory cells in tissues pose significant challenges for manual evaluation of tissue sections.
Manual evaluation of histologic tissue sections by a pathologist can be limited in several ways: (i) manual counting of inflammatory cells and determining expression levels of inflammation modulators are subjective and prone to observer bias and human error; (ii) a manual evaluation cannot practically assess a whole tissue section; (iii) quantitation of complex inflammation modulator distribution metrics (i.e. distances between cells, distances between cells and tissue features, fractal patterns, lacunarity, etc.) are not possible; and (iv) quantitative assessments of associations between one or more markers of inflammatory cell types using serial tissue sections are not possible by manual counting.
Therefore, whole slide scanning and sophisticated image analysis programs should be used to overcome many of the challenges presented by manual assessment of inflammatory cells and inflammation modulators in tissue sections. These methods will detect and characterize cells across entire tissue sections and, thus, quantitatively evaluate cells, cell subpopulations, and biomarker expression within the context of the greater tissue area. Ultimately, the inflammatory cell and inflammatory modulator landscape captured and analyzed by this approach can be used to draw inferences relevant to patient care in oncology. This paradigm will have utility for patient prognosis. Based on knowledge of the biologic mechanisms of the immune system-tumor interaction, it is likely that this paradigm can be applied to selecting patients for specific therapies and for monitoring treatment efficacy or toxicity.
Thus, there is a present and continuing need for image analysis-based systems and methods which can be used to quantitatively assess the patient-specific immune system state in tumor tissue and other relevant tissue compartments.